While, how to generate discriminative component-based multi-labels?
This is a very critical issue. If we need to consume a lot of manpower and material resources to obtain component labels, then this work will lose most of its meaning.
Fortunately, we find a simple-but-effective approach, that is, herein with reference to the natural language processing task commonly used hierarchy dictionary, WordNet [1]}. We can query and obtain the corresponding components that constitute the target by entering the target name.
Specifically, WordNet is a well-created toolkit, just like scikit-learn [2]}, we just need to call it when using it.
Inputting the label semantics (e.g., airplane), then outputting the new component labels (e.g., accelerator, wing, windshield). We give an example in Algorithm .
The complete process only takes no more than 2 seconds in our Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz 3.19 GHz.
Compared with the related approaches that rely on the annotated multi-labels, such as [3]} [4]}, our strategy is extremely flexible which can be easily applied in reality (we discuss the details in Section REF ).
